Personalized music similarity measure in multimodal music search – 1 – SIGIR’09



With the continuing advances in data storage and communication technology, there has been an explosive growth of music information from different application domains. As an effective technique for organizing, browsing, and searching large data collections, music information retrieval is attracting more and more attention. How to measure and model the similarity between different music items is one of the most fundamental yet challenging research problems. In this paper, we introduce a novel framework based on a multimodal and adaptive similarity measure for various applications. Distinguished from previous approaches, our system can effectively combine music properties from different aspects into a compact signature via supervised learning. In addition, an incremental Locality Sensitive Hashing algorithm has been developed to support efficient retrieval processes with different kinds of queries. Experimental results based on two large music collections reveal various advantages of the proposed framework including effectiveness, efficiency, adaptiveness, and scalability.

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